Response Surface Methodology

TL;DR: The turning process involves a large number of variables that affect its performance. Speed, feed and depth of cut have significant affect on feed force, thrust force, cutting force and surface finish. RSM is used to accurately predict the mathematical models to estimate feed force, thrust force, cutting force and surface roughness.

Abstract: In turning process, engineers are often faced with the problems of relating the cutting parameters (Speed, feed, depth of cut) to the output responses(Feed force, Thrust force, Cutting force, Surface roughness) to optimize the process. Also, Turing is done with the aim of achieving the best surface finish. But without optimization, it is not possible to automate the process. Turning process involves a large number of variables that affect its performance. However, based on the literature survey and the pilot experiments, it is found that speed, feed and depth of cut have significant affect on the feed force, thrust force, cutting force and surface finish. Therefore in the present work, they are considered as the decision variables and the feasible ranges of the decision variables are found by conducting trial experiments on INCONEL 600. In view of the costly process and the material, Design of Experiments (DOE) is used to reduce the number of experiments needed to carry out the analysis with the same accuracy and the precision. Then Response Surface Methodology (RSM) is applied to accurately predict the mathematical models to estimate feed force, thrust force, cutting force and surface roughness. Then these predicted models may be further used to optimize the turning process. Keywords— Hard Turning process, Inconel, Response surface methodology.